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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/52015

Title: BAYESIAN HIERARCHICAL ANOVA MODEL OF STOCHASTIC SEASONALITY FOR DIODON HOLOCANTHUS IN NORTHERN TAIWAN
Authors: Hungyen Chen
Kwang-Tsao Shao
Hirohisa Kishino
Contributors: 國立臺灣海洋大學:海洋文化研究所
Keywords: Bayesian inference
impingement sampling
nuclear power plant
spiny puffer
stochastic seasonality
Date: 2016-05
Issue Date: 2019-01-15T03:18:04Z
Publisher: Journal of Marine Science and Technology-Taiwan, .
Abstract: Abstract: Detecting seasonal variation in ecological phenomena is not
always possible by use of traditional methods. The pattern of
seasonality fluctuates because of the stochasticity of environmental factors, correlations among organisms, and human activity. Here, we propose a Bayesian hierarchical analysis of
variance (ANOVA) model of stochastic seasonality for detecting the annual fluctuation of seasonality. Using 11-year data
recorded monthly at 2 nuclear power plants in northern Taiwan,
we examined monthly fluctuations in fish species. To illustrate
the performance of the proposed model, we conducted a simulation that reflects the varying seasonality of species. The
most dominant species, Diodon holocanthus, exhibited a shift
in peak abundance from March to July that the traditional
ANOVA model failed to detect. Our results suggest that impingement data obtained from the intakes of power plants are
useful in studying the long-term temporal variation of fish
species. The proposed model provides an in-depth examination
of temporal variation for ecological analysis.
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/52015
Appears in Collections:[海洋文化研究所] 期刊論文

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